Title PM2.5: Comparison of modelling and measurements Presented by Hilde Fagerli SB, Geneva, September 7-9, 2009

OUTLINE Meteorologisk Institutt met.no To what extend do the models in use reproduce the background PM2.5 measurements? What are the main systematic biases and unknowns? What kind of mistakes in policy advice could the models be accountable for?

PM2.5 in the EMEP Unified Model Meteorologisk Institutt met.no Anthropogenic SIA: SO 4 2-, NO 3 -, NH 4 + PPM 2.5 : (OC, EC*, dust)‏ Natural Sea salt Mineral dust Water Emissions EMEP (SO x, NO x, NH 3, NMVOC, PM2.5, PM10 EC/OC factors based on Kupiainen & Klimont, 2006 Parameterisations of production in the model EQSAM

The recent changes in model runs affecting PM results Meteorologisk Institutt met.no Change of meteorological driver – from 10-year old HIRLAM version (PARLAM 50 km) to up-to-date version of HIRLAM (0.2x0.2º)‏ Resulted in concentration decrease for all aerosols, e.g. PM 2.5 is 20 to 40% lower Model revision – revised scheme for night-time formation of HNO3 Resulted 10-35% decrease of NO 3 and NH 4

Meteorologisk Institutt met.no ?5 - 10dust 8 (12)‏< 5 (10-20 coast)‏ Na + ? - 30 (24)‏ - 28 (35)‏ - 44 (-13)‏ - 34 (7)‏ ? Bias% PPM NH water NO SO SIA Relative contributions to PM2.5 based on model calculations for 2007 (SOA excluded)‏ PM2.5: Bias = -41% (-23)‏ In brackets: 2006 results with PARLAM-PS meteorology and ACID chemistry Note that NO3- and NH4+ are filter pack measurements

Meteorologisk Institutt met.no In Tsyro et al. (2007), m odel calculated EC were compared with observations from EMEP EC/OC and CARBOSOL campaigns for July 2002 – Oct 2004  EC was underestimated by 30% on average  The results consistently indicated possible inaccuracies in EC/OC emission estimates from wood burning: overestimation for northern countries underestimation for southern countries  The results were not so conclusive with regard to EC (PM) emissions from road traffic and other mobile sources, as we did not have enough information to draw conclusions from… Primary PM

Seasonal analysis: winter Meteorologisk Institutt met.no The results suggest: overestimation of wood burning emissions in northern Europe underestimation of wood burning emissions in central/southern Europe Emissions spatial distribution … ? Unaccounted local sources … ? EC underestimation by 30-60% at 7 sites in central and southern Europe Main sources: road traffic and other mobile sources Our results indicate that these emissions may be underestimated Problems with dispersion? Other sources?.... Forest fires Agricultural burning Seasonal analysis: summer Extra info

Model bias for SIA (2007) Meteorologisk Institutt met.no

EMEP intensive measurements: June-06 Jan-07 Meteorologisk Institutt met.no ES17 !!! only 2-3 days with data per month

Model bias for PM2.5 and SIA for 2007 (only 3 EMEP sites) ‏ Meteorologisk Institutt met.no *) SIA includes also coarse aerosols SIA Low modelled PM2.5: No SOA, underestimated SIA

Meteorologisk Institutt met.no Water Accuracy depends on the accuracy of SIA calculations Lack of measurements for verification Natural On average - minor components of PM2.5 Not regulated, but necessary for PM2.5 mass closure sea salt - intensive data show a considerable underestimation which is not seen in EMEP monitoring sites – look at data dust – practically no observations chemical speciation (Ca, Mg, K…) would help

Meteorologisk Institutt met.no Contribution of OC to PM1 From Zhang et al, 2007 Pie charts show the average mass concentration and chemical composition: Organics: Green, sulfate (red), nitrate (blue), ammonium (orange) and chloride (purple)‏

SOA SOA theories/models still changing rapidly and dramatically Still strong need to constrain theories/models with ambient measurements /14C, levoglucosan, AMS, etc cf EMEP, EUCAARI campaigns Examples: estimates of global BSOA production: –0-180 Tg(C)/yr (Best estimate: 88) Hallquist et al, 2009 –9-50 Tg(C)/yr Kanakidou et al., 2009 Note that the best estimate of Hallquist et al. lies outside the range of uncertainty in the Kanakidou estimate

Uncertainty in SOA modelling Results from the EMEP model with different VBS-based SOA approaches. BSOA: Biogenic SOA, ASOA: anthropogenic SOA, WOOD: OC from domestic/residential wood-burning, FFUEL: OC from fossil-fuel sources, GBND: background OC.

Overview PM 2.5 ?SOA ?Water ?Dust ≈0 (?)‏Na+ NegativeNH 4 + NegativeNO 3 - NegativeSO 4 2- NegativeSIA ?PPM2.5 BiasComponent

Implications for policy Meteorologisk Institutt met.no Variable performance (or unknown) of model results for PM constituents and/or missing components results in inaccurate calculations of PM2.5 chemical composition Difficult to design the optimal reduction strategy Underestimation of the background levels of PM2.5 not stringent enough emission reduction measures too little effective formation of SIA => underestimate effect of emission reductions (?)‏ affects calculations of SR relationships and scenarios (not enough long range transport?)

The end

Comparison with EMEP observations for 2006 Meteorologisk Institutt met.no Bias RRMSEModObsNsite PM NH Na NO SO SIA Note: Aerosol model based on ACID.rv2_7_10; PARLAM meteorology

Meteorologisk Institutt met.no  Quality of emission data for PPM2.5 is crucial for the accuracy of model results for PPM2.5  Sound description of removal processes, esp. wet scavenging  Boundary conditions (?) ‏  Primary PM: What is needed for improvement of modelling:

Meteorologisk Institutt met.no  SO4 formation…  NO3 formation….  NH4 formation…  Sound description of removal processes, esp. wet scavenging  Boundary conditions (?) ‏  Appropriate observations for validation of results SIA: What is needed for improved modelling

Comparison with EMEP observations for 2007 Meteorologisk Institutt met.no Bias RRMSEModObsNsite PM NH Na NO SO SIA Note: filter-pack measurements for SIA components and Na, i.e. no size cut-off PM2.5 measurements: not all sites use reference gravic method